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A Comprehensive Review of Spiking Neural Networks: Interpretation, Optimization, Efficiency, and Best Practices
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Biological neural networks continue to inspire breakthroughs in neural network performance. And yet, one key area of neural computation that has been under-appreciated and under-investigated is biologically plausible, energy-efficient spiking neural networks, whose potential is especially attractive for low-power, mobile, or otherwise hardware-constrained settings. We present a literature review of recent developments in the interpretation, optimization, efficiency, and accuracy of spiking neural networks. Key contributions include identification, discussion, and comparison of cutting-edge methods in spiking neural network optimization, energy-efficiency, and evaluation, starting from first principles so as to be accessible to new practitioners.
Forward citations
Cited by 4 Pith papers
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TDFormer: A Top-Down Attention-Controlled Spiking Transformer
A top-down feedback module for spiking transformers improves temporal information flow, reduces temporal vanishing gradients, and reaches 86.83% top-1 accuracy on ImageNet.
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Efficient EEG Seizure Detection Using INT8 Quantization, Channel Pruning, and Spiking Neural Networks
On a shared 1D-CNN baseline for CHB-MIT seizure detection, INT8 quantization cut model size from 1.63 to 0.44 MB and latency by 2.8x with preserved AUC, while SNN conversion was 288x slower on CPU.
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DRiVE: Dynamic Recognition in VEhicles using snnTorch
DRiVE, a feedforward spiking neural network built with snnTorch, is applied to binary vehicle detection, reporting 94.82% accuracy, but this number conflicts with the paper's own confusion matrix and no independent be...
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Contemporary implementations of spiking bio-inspired neural networks
A review of CMOS, memristive, superconducting, and optical hardware for spiking neural networks, concluding that hybrid approaches are the most promising direction.
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